Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces

Abstract Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from im...

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Autores principales:	Jonathan C. Kao, Stephen I. Ryu, Krishna V. Shenoy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a9d7b426316a4e178dc3790053796953
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spelling	oai:doaj.org-article:a9d7b426316a4e178dc37900537969532021-12-02T16:06:09ZLeveraging neural dynamics to extend functional lifetime of brain-machine interfaces10.1038/s41598-017-06029-x2045-2322https://doaj.org/article/a9d7b426316a4e178dc37900537969532017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06029-xhttps://doaj.org/toc/2045-2322Abstract Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.Jonathan C. KaoStephen I. RyuKrishna V. ShenoyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-16 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jonathan C. Kao Stephen I. Ryu Krishna V. Shenoy Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
description	Abstract Intracortical brain-machine interfaces (BMIs) aim to restore lost motor function to people with neurological deficits by decoding neural activity into control signals for guiding prostheses. An important challenge facing BMIs is that, over time, the number of neural signals recorded from implanted multielectrode arrays will decline and result in a concomitant decrease of BMI performance. We sought to extend BMI lifetime by developing an algorithmic technique, implemented entirely in software, to improve performance over state-of-the-art algorithms as the number of recorded neural signals decline. Our approach augments the decoder by incorporating neural population dynamics remembered from an earlier point in the array lifetime. We demonstrate, in closed-loop experiments with two rhesus macaques, that after the loss of approximately 60% of recording electrodes, our approach outperforms state-of-the-art decoders by a factor of 3.2× and 1.7× (corresponding to a 46% and 22% recovery of maximal performance). Further, our results suggest that neural population dynamics in motor cortex are invariant to the number of recorded neurons. By extending functional BMI lifetime, this approach increases the clinical viability of BMIs.
format	article
author	Jonathan C. Kao Stephen I. Ryu Krishna V. Shenoy
author_facet	Jonathan C. Kao Stephen I. Ryu Krishna V. Shenoy
author_sort	Jonathan C. Kao
title	Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
title_short	Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
title_full	Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
title_fullStr	Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
title_full_unstemmed	Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
title_sort	leveraging neural dynamics to extend functional lifetime of brain-machine interfaces
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/a9d7b426316a4e178dc3790053796953
work_keys_str_mv	AT jonathanckao leveragingneuraldynamicstoextendfunctionallifetimeofbrainmachineinterfaces AT stepheniryu leveragingneuraldynamicstoextendfunctionallifetimeofbrainmachineinterfaces AT krishnavshenoy leveragingneuraldynamicstoextendfunctionallifetimeofbrainmachineinterfaces
_version_	1718385109811331072

Leveraging neural dynamics to extend functional lifetime of brain-machine interfaces

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